What is Agentic AI? Benefits, Risks, and Outlook

Agentic AI refers to artificial intelligence systems that are designed to independently carry out complex tasks with little or no human supervision. 

At the core of agentic AI is the concept of an AI agent: a piece of software that, when added on to a traditional large language model, allows it to make decisions and act on them with a human-like degree of autonomy.

Say you want to create a marketing campaign using artificial intelligence. A traditional AI model might respond to the prompt “create a marketing campaign” by generating ads and ad copy. An agentic system, on the other hand, would not only generate the content; it would coordinate with the ad platforms, schedule the posts, track the metrics, and make adjustments to the strategy in real time, without needing to be prompted at each step.

While these types of systems are still in their infancy, exciting developments are happening every week. 

For example, Amazon, Google, Perplexity, and PayPal have recently added agentic shopping features to their platforms that will find and purchase items from the wider internet without the user having to leave the platform.

And Google and Opera have recently joined OpenAI and Anthropic in the race to perfect agents that can use browsers to perform complex tasks like checking and responding to emails or shopping for an outfit in response to a prompt. (You can try out a demo version of a browser agent here.)

Of course, each of these examples still requires a high level of human guidance. Real-world examples of truly autonomous AI agents are still rare, and the ones that do exist have limited capabilities and can be prone to error. But the potential ramifications of widespread use of agentic AI are too serious to ignore:

Adoption of agent-like systems is already widespread in the tech sector—48% of tech executives are already adopting or fully deploying agentic AI, according to a survey from Ernst & Young. And, according to a Salesforce survey, human resources officers are predicting that this digital labor will allow them to redeploy a quarter of their employees to new roles or teams over the next couple of years.

According to Gartner®,  “[by] 2028, AI agent machine customers will replace 20% of the interactions at human-readable digital storefronts, and that “by 2035, 80% of internet traffic could be driven by AI Agents.”If autonomous AI systems do surpass humans as the dominant users of the internet, they will certainly bring with them new security challenges.

There are various types of AI agents of varying degrees of complexity and with different underlying operating paradigms.

But at their most basic, today’s AI agents consist of a conventional LLM or multimodal system that has been wrapped in “scaffolding” software. That wrapper in turn controls the LLM and allows it to interface with the external world.

To allow the underlying LLM to behave independently, the software wrapper usually functions using some version of the autonomy loop principle: It prompts the LLM through a feedback cycle of perception, planning, memory use, and action that is modeled on the way humans reason and approach complex tasks.

Broadly speaking, here are some other key features that will distinguish agentic systems from other forms of AI:

Goal-oriented autonomy

Instead of reacting to inputs in a linear fashion, a goal-based agent sets an objective first, then works backward to figure out how to achieve its goal.

Iterative planning & self-correction

An agentic AI system can adapt its plans to changing circumstances, and it can recall past feedback it has received to learn over time how to better avoid repeating its “mistakes.”

Tool / API orchestration

A key distinguisher of agentic AI from other varieties is its ability (sometimes known as tool calling) to independently use multiple external pieces of software or other tools without human prompting.

Long-term memory

An AI agent with long-term memory can store information on a permanent basis, allowing it to accrue “experience” and tailor its behavior over time instead of starting each interaction from scratch.

Multi-agent collaboration

When more than one AI agent works together to complete a task, they are participating in a multiagent system (MAS).

As mentioned earlier, one of the key differences between agentic AI and more traditional generative systems is agentic AI’s emphasis on performing tasks rather than creating content.

Generative AI’s major function is to generate new content, like text, images, or music, from scratch. In contrast, agentic AI’s emphasis is on autonomously executing tasks, often using multiple pieces of software, similar to the way a human would use a computer.

Here’s another way to think about it: generative AI merely paints the picture, while agentic AI also contacts the gallery and sets up the art opening. (Or in a multiagent system, the artist’s agent might pick the gallery and send the pictures to the gallery’s own agent, which in turn books the opening night and sends out the invitations without needing to be asked).

The dream of a truly autonomous, general-purpose AI system that can swiftly fulfill any open-ended request has yet to be realized (although agents are getting extremely good at playing Minecraft and Pokémon Red by themselves).

However, there are enormous resources being invested to ensure that the dream keeps moving toward reality, and today there are plenty of examples of systems that act with at least some degree of autonomy:

Travel Concierges

Agentic AI travel assistants are beginning to show promise, though at the current stage, they offer only partial autonomy.

GuideGeek, for example, is an AI-powered travel chatbot that provides personalized travel recommendations via platforms like Instagram and WhatsApp. 

Similarly, Booked.ai automates booking and itinerary planning through a conversational interface.

While these services can streamline complex planning, they still rely on human review or confirmation for many actions, making them semi-agentic rather than fully autonomous.

Supply-Chain Optimizers

Some logistics platforms are beginning to incorporate agent-like behaviors to improve efficiency.

For instance, DHL uses AI-based route optimization to adapt delivery plans based on real-time weather, traffic, and performance data.

While these kinds of systems perform a degree of task automation, they still usually operate under tight human-defined constraints and don’t exhibit full autonomy in planning or execution.

Agentic Commerce

AI-driven commerce tools are rapidly becoming more common, with 88% of shoppers using AI in some form during the 2024 holiday retail season and over half reporting greater satisfaction from the extra support these technologies provide.

These capabilities still require plenty of human prompting, but further out on the cutting edge, experiments with AutoGPT have demonstrated autonomous agents running mock e-commerce businesses entirely on their own. Emerging examples of agentic commerce include:

• Amazon’s AI-powered “Buy for Me” feature allows a user to browse and make purchases from other online stores without leaving Amazon’s platform; when a user has selected an item, Amazon’s agent visits the external site and handles the checkout and payment details with minimal input.
• Google recently announced its plans to roll out a feature similar to Amazon’s that will allow users to make purchases directly from their search results, with an AI agent handling the transaction on the vendor’s website.
• Perplexity Pro users can make purchases from directly within Perplexity’s chat interface. The company recently added a PayPal integration to make in-chat shopping more seamless.
• Visa and Mastercard recently launched initiatives to enable agents to more easily initiate transactions independently, within a user’s set spending limits. Both initiatives use tokenization to secure individual transactions; Visa’s Intelligent Commerce program includes a suite of APIs that will allow seamless integration of agent-initiated transactions, while Mastercard’s Agent Pay will initially focus on integrations with Microsoft’s ecosystem, as well as enabling B2B use cases. 

Cybersecurity Triage Bots

Cybersecurity applications show some of the most mature examples of agentic behavior. 

For example, Microsoft Security Copilot blends generative AI with real-time threat intelligence to assist analysts; continuously processes incoming data, and offers recommendations in a process that mirrors early-stage agentic reasoning.

These types of threat-detection tools do exhibit some of the hallmark traits of agentic AI, like goal-driven autonomy, context awareness, and API orchestration; but they still require human operators to validate or authorize certain actions.

However, as agentic AI continues to rapidly mature, the coming generation of cybersecurity tools is poised to deliver fully autonomous end-to-end threat detection and response. Agentic cybersecurity tools will be able to pick up on a threat, decide how to handle it and take action, all with minimal need for human intervention.

Agentic AI promises to be transformative for businesses. However, most of these benefits are still theoretical or are predictions that have been extrapolated from early-stage versions of the systems in question.

Productivity Gains

AI can take over repetitive tasks, giving employees more time to focus on creative or strategic work. 

In customer service, for example, AI can already help draft replies or route tickets faster. While this isn’t true agentic AI, it’s a step toward it.

In 2023, McKinsey & Company predicted that AI could eventually add $2.6 to $4.4 trillion in value annually across the various use cases analyzed in the study (some of which were specific to AI agents).

Cost Savings

AI tools can help cut costs by automating parts of a workflow. For example, automating support ticket triage can reduce how long customers wait for a response. 

However, cost savings depend on how accurate the system is, how much it costs to maintain, and how much human oversight is needed; McKinsey’s report also highlights that to see real savings at scale, businesses often need to make big changes to how they work.

Always-On Availability

AI systems can run 24/7, doing things like checking server logs overnight or answering basic questions when staff are offline.

While truly autonomous agents that can handle entire workflows without help are still rare, existing tools like chatbots and system monitors show how AI can support round-the-clock operations.

Faster Decision-Making

AI can help businesses make faster decisions by quickly analyzing data and suggesting actions. This is common in fraud detection or inventory management systems.

Easier to Scale

AI can help businesses grow without needing to hire at the same pace. A single AI assistant can already support several departments by managing schedules, creating reports, or drafting content. So it stands to reason that when agentic AI assistants reach maturity, they will be even more flexible than today’s generative AI assistants.

What to Expect in the Short Term (2025–2027)

• Widespread Adoption—  A study by Deloitte predicts that over the course of 2025, a quarter of the companies that are already using generative AI will launch pilot programs for agentic systems; and that proportion will grow to half by 2027.
• Agent-to-Agent Interaction—Today, if multi-agent collaboration happens at all, it’s likely among agents that are built to operate within a single company. But soon, it’s likely that AI agents will operate across the internet, with agents from one platform communicating with agents from another. For example, instead of a user shopping on Walmart’s website, that user’s personal agent could interact with Walmart’s own AI agent to automate the entire shopping process.
• Standardization—In order for AI agents to navigate the web and interact with one another safely and efficiently, protocols will have to be created and adopted. These will likely be created by AI standards bodies or research groups such as OWASP, MIT’s NANDA, or AGNTCY.
• Increased Autonomy—As agentic technology matures, it will become more independent and need less direction from its users. To put the evolution in Hollywood terms, you might say that your AI is going to get promoted from your personal assistant to your professional agent; rather than buying your socks and ordering your coffee, an AI agent will represent your interests and handle your affairs in the world at large.
• Growing Threats—As agentic AI becomes more capable and more independent, bad actors are likely to come up with sophisticated new ways to trick, exploit, and otherwise misuse the technology

Looking Ahead to the Long Term (2028–2030)

Further out, we may see the rise of more complex ecosystems where multiple AI agents can talk to each other, share data, and work together across departments or even different companies.

These future systems could coordinate on tasks like supply chain optimization, contract negotiation, or customer service handoffs. While this level of automation doesn’t exist today, researchers and startups are exploring what’s possible.

To keep these systems safe, new guardrails will be needed. That includes tools for real-time monitoring, explaining how decisions are made, and limiting what agents can access or do without permission.

Potential security threats from autonomous AI are already being studied by organizations like OWASP and IEEE.

As agentic AI grows, governments and industry leaders are expected to push for stronger rules on things like transparency, audit trails, and informed consent.

Standards bodies such as NIST are already publishing frameworks to guide responsible AI use. These efforts will be critical to ensure agents are safe, reliable, and interoperable, similar to how web or mobile standards helped grow those ecosystems responsibly.

What is Agentic AI?

Agentic AI refers to artificial intelligence systems designed to operate with a high degree of autonomy. Unlike traditional AI that responds to individual prompts, agentic AI can independently plan, make decisions, and execute tasks across different software platforms—similar to how a human would complete a complex assignment.

How does agentic AI differ from generative AI?

While generative AI focuses on creating content like text, images, or music, agentic AI focuses on completing tasks. For example, generative AI might write an article, whereas agentic AI would send that article to an editor or publish it online without human intervention.

What are the core features of agentic AI systems?

• Goal-oriented autonomy: Plans backward from a defined goal. 
• Iterative planning & self-correction: Learns from mistakes and adapts. 
• Tool/API orchestration: Uses external programs independently. 
• Long-term memory: Retains knowledge for future tasks. 
• Multi-agent collaboration: Works with other AI agents in teams.
  

Are there any real-world examples of agentic AI today?

Yes, though most systems are still early-stage:

• Travel: Booked.ai and GuideGeek offer semi-autonomous travel planning. 
• Supply Chain: DHL uses agentic-like AI for route optimization. 
• E-commerce: Shopify’s Sidekick helps automate online store tasks. 
• Cybersecurity: Microsoft Security Copilot uses real-time data to suggest actions.
  

What are the benefits of agentic AI for businesses?

• Productivity gains: Automates repetitive tasks. 
• Cost savings: Reduces operational overhead. 
• 24/7 availability: Works continuously without fatigue. 
• Faster decision-making: Quickly analyzes data and offers actions. 
• Scalability: Supports business growth without proportional staffing.
  

What are the risks or challenges of agentic AI?

• Security threats: Autonomous systems may introduce novel vulnerabilities. 
• Lack of oversight: Fully independent agents could act in unintended ways. 
• Error propagation: Mistakes can scale if not caught early. 
• Ethical concerns: Transparency, control, and accountability remain unresolved.
  

How do agentic AI systems work?

They usually combine a large language model (LLM) with a scaffolding framework that controls feedback loops involving perception, planning, memory, and action. This enables independent reasoning and execution.

Is agentic AI widely available to the public yet?

While fully autonomous agents are still experimental, companies like Amazon, Google, Amazon, Open AI, and Perplexity are already using features of agentic AI to automate shopping and other tasks with reduced manual effort. These early agentic systems still require significant human input, but these technologies are becoming more advanced and adoption is spreading rapidly.

Gartner, Top Strategic Technology Trends for 2025: Agentic AI, 21 October 2024
Gartner, Gartner Futures Lab: The Future of Identity, 7 April 2025
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